Wrap your head around functional programming!

functional-programming.js

/**
 * Functional Programming!!!
 */
const val = 5,

  /**
   * The identity function takes a function and returns a copy of that function.
   * The use of the spread (...) operator here is key to understanding more
   * complex functional programming, like curry() below.
   */
  identity = f => (...args) => f(...args),

  /**
   * Here are three of the key functions to understand for functional programming.
   *
   * curry() takes a non-unary function that accepts multiple functors and returns
   * a function that accepts exactly one functor
   *
   * pipe() accepts a set of transform functions and returns a function that applies
   * each of these transforms, one at a time, to the given functor
   *
   * compose() works just like pipe(), just in reverse. For example, given the composed
   * function f(g(h(x))), you can rewrite it as a pipe:
   *
   *   pipe(h, g, f)
   *
   * or as a composition:
   *
   *   compose(f, g, h)
   *
   * Notice that compose follows the same order as if you were writing out the complete
   * chain, while pipe requires the transforms to be in the order you would like to
   * apply them, much like gates filter material flowing through a pipe.
   */
  curry = (f, arr = []) => (...args) =>
    (a => (a.length === f.length ? f(...a) : curry(f, a)))([...arr, ...args]),
  pipe = (...funcs) => x => funcs.reduce((v, f) => f(v), x),
  compose = (...funcs) => x => funcs.reduceRight((v, f) => f(v), x),

  /**
   * Here are some basic unary transformation functions - they take
   * a single functor (input) and transform & return it
   */
  double = x => x * 2,
  square = x => x * x,
  half = x => x / 2,

  /**
   * This transformation function is configurable via a parameter,
   * so instead of just passing it directly you'll actually want to call
   * it with a value and pass the function it returns - now you're thinking
   * functionally!
   */
  add = x => y => x + y,

  /**
   * Here's a basic non-unary function - it doesn't fit too well in a pipe/
   * compose function, so we'll curry it to split it up into a chain of unary
   * functions!
   *
   * curryedSum can be called in a few different ways:
   *
   *   curryedSum(1, 2, 3) // 6
   *   curryedSum(1, 2)(3) // 6
   *   curryedSum(1)(2, 3) // 6
   *   curryedSum(1)(2)(3) // 6
   *
   * You can also partially compose it, as such:
   *
   *   let foo = curryedSum(1, 2) // x => 1 + 2 + x
   *   let bar = curryedSum(1)    // x => 1 + x + y
   * and then use it in a pipe:
   *
   *   pipe(foo)
   *   pipe(bar(2))
   */
  sum = (x, y, z) => x + y + z,
  curryedSum = curry(sum),

  /**
   * Here's a basic unary trace function - it simply logs the current value
   * it is passed with a tag.
   */
  trace = tag => x => {
    console.log(`  [${tag}] => ${x}`)
    return x
  },

  /**
   * The following transform functions will all have the same result!
   */
  transform = x => double(square(add(2)(x))),
  composedTransform = compose(double, square, add(2)),
  pipedTransform = pipe(add(2), square, double),
  pipedTracedTransform = pipe(
    trace('initial'),
    add(2),
    trace('add(2)'),
    square,
    trace('square'),
    double,
    trace('double')
  )

console.log()
console.log(`Transform takes a number (${val}) and performs the following`)
console.log('operations:')
console.log('  - adds 2')
console.log('  - squares the value')
console.log('  - doubles the value')
console.log()
console.log('        transform: ' + transform(val))
console.log('composedTransform: ' + composedTransform(val))
console.log('   pipedTransform: ' + pipedTransform(val))
console.log()
console.log('By simply adding a trace() call to the pipe, we can watch the')
console.log('transformation at each step, like so:')
console.log()
console.log('\n returned value: ' + pipedTracedTransform(val))
console.log()